Browsing by Author "Gribok, Andrei"
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- ItemDecision tool for the early diagnosis of trauma patient hypovolemia.(2008-06-05) Chen, Liangyou; McKenna, Thomas M; Reisner, Andrew T; Gribok, Andrei; Reifman, JaquesWe present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital when reliable acquisition of vital sign data may be difficult The decision tool uses basic vital sign variables as input into linear classifiers which are then combined into an ensemble classifier The classifier identifies hypovolemic patients with an area under a receiver operating characteristic curve AUC of 0 76 standard deviation 0 05 for 100 randomly reselected patient subsets The ensemble classifier is robust classification performance degrades only slowly as variables are dropped and the ensemble structure does not require identification of a set of variables for use as best feature inputs into the classifier The ensemble classifier consistently outperforms best features based linear classifiers the classification AUC is greater and the standard deviation is smaller pUnder0 05 The simple computational requirements of ensemble classifiers will permit them to function in small fieldable devices for continuous monitoring of trauma patients
- ItemDiagnosis of hemorrhage in a prehospital trauma population using linear and nonlinear multiparameter analysis of vital signs.(2007-11-16) Chen, Liangyou; Reisner, Andrew T; McKenna, Thomas M; Gribok, Andrei; Reifman, JaquesIn this study we analyzed a dataset of time series vital signs data collected by standard Propaq travel monitor during helicopter transport of 898 civilian trauma casualties from the scene of injury to a receiving trauma center The goals of the analysis are two fold First to determine which combination of the automatically collected and qualified vital signs provides the best discrimination between casualties with and without major hemorrhage Second to determine whether nonlinear classifiers provide improved discrimination over simpler linear classifiers Major hemorrhage is defined by the presence of injuries consistent with hemorrhage in casualties who received one or more units of blood We randomly selected a subset of the casualties to train and test the classifiers with multiple combinations of the vital signs variables and used the area under the receiver operating characteristic curve ROC AUC as a decision metric Based on the results of 100 simulations we observe that i the best two features obtained are systolic blood pressure and heart rate mean AUC 0 75 from a linear classifier and ii the use of nonlinear classifiers does not improve discrimination These results support earlier findings that the interaction of systolic blood pressure and heart rate is useful for the identification of trauma hemorrhage and that linear classifiers are adequate for many real world applications
- ItemPredictive monitoring for improved management of glucose levels.(2009-11-03) Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei; Ward, W KennethRecent developments and expected near future improvements in continuous glucose monitoring CGM devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels This article assesses the feasibility of data driven models to serve as the forecasting engine of predictive monitoring systems
- ItemRegularization of body core temperature prediction during physical activity.(2007-10-23) Gribok, Andrei; McKenna, Thomas; Reifman, JacquesThis paper deals with the prediction of body core temperature during physical activity in different environmental conditions using first principles models and data driven models We argue that prediction of physiological variables through other correlated physiological variables using data driven techniques is an ill posed problem To make predictions produced by data driven models accurate and stable they need to be regularized We demonstrate on data collected during laboratory study that data driven models if regularized properly can outperform first principles models in terms of accuracy of core temperature predictions We also show that data driven models can be made portable from one subject to another thus making them a valuable practical tool when data from only one subject is available to train the model